Behaviour Therapy Intervention to Improve Mental Health in U

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Therapy Intervention to Improve Mental Health in. University Students. Helen M Stallman,1 David J Kavanagh,2 Anthony R Arklay3 and James Bennett-Levy4.
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ORIGINAL ARTICLE

Randomised Control Trial of a Low-Intensity Cognitive-Behaviour Therapy Intervention to Improve Mental Health in University Students Helen M Stallman,1 David J Kavanagh,2 Anthony R Arklay3 and James Bennett-Levy4 1

School of Psychology, Social Work and Social Policy, University of South Australia, 2Institute of Health and Biomedical Innovation and School of Psychology and Counselling, Queensland University of Technology, 3University Health Service, The University of Queensland, and 4University Centre for Rural Health (North Coast), University of Sydney

Objective: University students have high rates of clinical and subclinical depression and anxiety symptoms, low rates of face-to-face helpseeking, and high rates of Internet use. Low-intensity cognitive-behaviour therapy (LI-CBT) that incorporates e-resources has potential for increasing access to help by distressed students. Method: This article reports the first randomised controlled trial of LI-CBT in a university context, comparing it with self-help information only. Results: Only 11% of distressed students agreed to participate in treatment, and only 58% of LI-CBT participants attended any sessions. Almost all of the 107 participants were female, with an average age of 23 and high average distress. Intention-to-treat analyses using mixed models regressions showed that LI-CBT participants had greater reductions in depression and anxiety than controls who received self-help information only, but only over the first 2 months. Correction for baseline levels eliminated these effects, although differential improvements for anxiety and stress were seen if analyses were restricted to LI-CBT participants who attended sessions. LI-CBT also resulted in differential reductions in perceived connection to the university perhaps because of greater usage of staff resources by controls. Conclusions: Results provide some support for a potential role for LI-CBT within universities, but suggest that marketing and engagement strategies may need refinement to maximise its uptake and impact. Key words: anxiety; CBT; connectedness; depression; low intensity; university students.

What is already known on this topic

What this paper adds

1 University students have a high prevalence of psychological distress that impacts on learning. 2 Relatively few students access existing face-to-face services for mental health problems. 3 Low-intensity cognitive-behaviour therapies (LI-CBTs) in community health services have demonstrated substantial effect sizes.

1 Participants who attended an LI-CBT session had better outcomes on anxiety and stress than did those receiving self-help information only. 2 Surprisingly, LI-CBT resulted in lower perceived connection to the university perhaps because of greater use of staff resources by controls. 3 Most distressed students did not volunteer to participate, and only 58% of consenting participants in LI-CBT attended any sessions, suggesting a need for improved marketing and engagement.

Correspondence: Helen Stallman, School of Psychology, Social Work and Social Policy, University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia. Fax: +61 8 8302 4377; email: [email protected] Conflict of interest: None. Trial Registration: The trial was registered with the Australian New Zealand Clinical Trials Registry (http://www.anzctr.org.au/; #ACTRN12611000114943). Accepted for publication 8 February 2015 doi:10.1111/ap.12113

Australian Psychologist (2015) 51 (2016) 145–153 © 2016 2015 The Australian Psychological Society

Consistent with international research (e.g., Eisenberg, Gollust, Golberstein, & Hefner, 2007), there is a high prevalence of mental health problems in Australian university students, with 83% reporting elevated levels of psychological distress (Stallman, 2010a). About half the students who present at university health services in Australia for physical health problems have levels of distress indicative of mental health problems, irrespective of the time in the academic year (Stallman, 2008; Stallman & Shochet, 2009). Psychological distress is associated with a reduced capacity for work and study, with significant implications for attainment of their academic goals. In a recent study, students who were highly distressed were on average unable to work or study for 8 days in the previous 4 weeks and 1 145

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on average had another 9 days of reduced capacity for work. In total, they had some work or study impairment for around 60% of the time (Stallman, 2008). As in the general population (Andrews, Henderson, & Hall, 2001), only around a third of students receive assistance for these problems (Stallman & Shochet, 2009). This is despite greater access than the general population to campus-based counselling and general practitioner services that are free to users. The high prevalence of distress makes it impossible to address using traditional face-to-face services for every student. Counselling services in Australia are also hampered to provide adequate support for students because of inadequate resources compared with their international counterparts (Downs, 2008; Jackson & Connelley, 2009; Stallman, 2011). For some students, attitudes towards services and stigma about mental disorders or service use may also inhibit uptake (Crisp, Gelder, Goddard, & Meltzer, 2005; Eisenberg, Speer, & Hunt, 2012). In the UK, low-intensity cognitive-behaviour therapies (LICBTs) have been evaluated in health service contexts with impressive results (Clark, 2011; Richards & Suckling, 2009; Smith, 2010). The goal behind the development of LI-CBTs has been to increase access to evidence-based psychological therapies using the minimum level of intervention necessary to create the maximum gain through: (a) reducing the amount of time the practitioner is in contact with individual clients; and (b) using practitioners specifically trained to deliver LI-CBT who may not have formal health professional or high-intensity CBT qualifications, but receive close intensive supervision from specialist mental health professionals (Bennett-Levy et al., 2010). In practice, LI-CBTs typically involve screening, triage, use of bibliotherapies or Internetbased treatments, and conduct of manualised brief treatments or group interventions. While the predominant form of LI-CBT in Australia has been Internet-based treatments (Christensen, Griffiths, & Jorm, 2004; Titov et al., 2011), there are some Australian examples of LI-CBTs using written (Kavanagh et al., 2010) and pictorial materials (Laliberte et al., 2010). Up to now, LI-CBT research has been conducted in general populations. To our knowledge, no study has as yet examined the impact of LI-CBTs in a university setting. The current study tests a local version of the UK model—using trained psychology graduate students, and focusing on screening and referral to online interventions and university workshops, combined with coaching that focuses only on the content of those online or group treatments. University health clinics have potential for engaging and triaging students into LI-CBT because around half of students who attend university health services for physical health problems report elevated levels of psychological distress (Stallman, 2008; Stallman & Shochet, 2009). While the siting of services for distress was seen as acceptable, students typically said they were attending the service for other health issues (Stallman, 2008; Stallman & Shochet, 2009). The current study therefore attempted to trial LI-CBT within a university health service, restricting the sample to students who were not seeking mental health treatment, and comparing LI-CBT with the provision of information about available services. 2 146

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Research Aims This study aimed to undertake a randomised controlled trial comparing LI-CBT with a self-help control in a university student sample. The primary outcomes were levels of selfreported depression, anxiety, and stress, and secondary outcomes were university connectedness, coping self-efficacy, and well-being.

Method Participants Recruitment took place from March to November 2011. Initially, we asked receptionists at The University of Queensland Health Service to invite all students presenting for a scheduled doctor’s appointment at either the urban or rural campus sites to complete the screening questionnaire on an iPad (Apple Inc., Apple Campus, Cupertino, CA, USA) in the Health Service waiting room while they waited for the appointment. Signs next to the iPad kiosks also allowed for passive recruitment. However, while 54 students completed the screening measure over the first 6 weeks, only three fulfilled criteria and entered the trial via this route. Accordingly, recruitment was then extended to the entire student body through advertising (campus posters and university website) and emails from heads of schools. Advertisements and emails contained a link to the online screening questionnaire. To be eligible volunteers had to: (a) be an enrolled student, (b) score ≥16 on the Kessler 10 (K10; Kessler et al., 2003) screening measure—a level that commonly applied in national epidemiological studies (Australian Bureau of Statistics, 2006, 2008); (c) not be currently receiving treatment for a mental health problem; and (d) consent to participation.

Measures The principal outcomes were depression, anxiety, and stress as measured by the 21-item version of the Depression, Anxiety and Stress Scales (DASS-21: Lovibond & Lovibond, 1995b). The DASS-21 has strong discriminant and concurrent validity (Lovibond & Lovibond, 1995a, 1995b). In the current sample, internal consistencies for the depression (DASS-Depression; α = .87), anxiety (DASS-Anxiety; α = .84), and stress (DASSStress; α = .82) subscales were very satisfactory. Secondary outcomes were university connectedness, coping self-efficacy, and well-being. University connectedness was measured using the University Connectedness Scale (Stallman, 2010b), a 19-item measure of a student’s perception of belonging and support within their university. It had strong internal consistency in this sample (α = .88). The Coping Self-Efficacy scale (CSE: Chesney, Neilands, Chambers, Taylor, & Folkman, 2006) is a measure of a person’s perceived ability to cope effectively with life challenges. The current study used the 26-item total score. In both Chesney et al. (2006) and the current sample, coefficient alpha for the total score was excellent (α = .95, .94, respectively). Well-being was assessed using the 5-item World Health Organization WellBeing Index (WHO5: Psychiatric Research Unit Frederiksborg General Hospital, 1998). The 5-item scale covers positive mood Australian Psychologist (2015) Australian Psychologist 51 (2016) 145–153 © 2016 2015 The Australian Psychological Society

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(good spirits, relaxation), vitality (being active and waking up feeling refreshed and rested), and general activities (being interested in things). The internal consistency of the WHO5 in the current sample was very sound (α = .82). Other measures at baseline included demographics and Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De la Fuente, & Grant, 1993).

Interventions Self-help control Participants in this condition received a single personalised email with information about counselling and academic workshops available at the university.

LI-CBT LI-CBT followed the Improving Access to Psychological Therapies (IAPT) manualised structure (Richards & Whyte, 2009). This structure comprised information gathering, assessment of risk, routine assessment (K10, work, and social adjustment), information review, medication review, low-intensity psychological therapy, shared decision making, and ending. Trained well-being coaches encouraged participants to implement a range of lowintensity treatments including behavioural activation, cognitive restructuring, medication support, exposure therapy, problem solving, and sleep hygiene. Well-being coaches also linked students with CBT-based online programs and supported their use of them, where relevant. These programs included http:// www.thedesk.org.au (for functional and emotional concerns of university students), http://www.anxietyonline.org.au and http://www.virtualclinic.org.au (for anxiety disorders), and http://www.moodgym.anu.edu.au (for depression). Coaches also informed participants about university-based programs, including library workshops on academic skills, counselling workshops (e.g., time management, procrastination), parenting programs, and disability or international student services. Participants considered at high risk for self-harm were referred to their general practitioner for treatment. Sessions were delivered via telephone or Skype® (Microsoft Corp., Redmond, WA, USA). Session 1 involved a biopsychosocial assessment of the client’s current needs and included a structured clinical interview using anxiety, mood, alcohol, and adjustment segments of the NetSCID (Telesage, 2011), an online version of the Structured Clinical Interview for DSM-IV Diagnoses (First, Spitzer, Gibbon, & Williams 1996). The practitioner and participant collaboratively developed an action plan based on results of the assessments, and participants received an emailed copy of their plan after the session. Participants were offered up to six LI-CBT sessions in addition to their assessment session. The number of sessions was decided collaboratively between the well-being coach and the participant in response to the participants needs, as per the IAPT model (Richards & Whyte, 2009). The structure of the LI-CBT sessions focused on reviewing progress, encouraging new skills and behaviours, reinforcing engagement in treatment activities, promoting self-regulation and self-efficacy, and relapse prevention. Australian Psychologist Psychologist 51 (2015) Australian (2016) 145–153 ©2016 2015The TheAustralian AustralianPsychological PsychologicalSociety Society ©

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Training and Treatment Fidelity Well-being coaches were first year postgraduate health psychology students. Training and supervision was provided by two experienced clinical psychologists with experience in lowintensity interventions. An initial intensive day of training included a theoretical background of low-intensity interventions, the mental health needs of university students, and active skills training in LI-CBT, using content adapted from the UK LI-CBT training (Richards et al., 2008; Richards & Whyte, 2009). Ongoing training, assessment of competencies, and supervision was conducted in accordance with IAPT guidelines (Richards, Chellingsworth, Hope, Turpin, & Whyte, 2010). Wellbeing coaches received an hour of weekly individual supervision, focused on client outcomes (Richards et al., 2010), and group supervision of 2 hr per week focused on well-being coach competencies and administrative issues.

Procedure The study received ethical clearance from the Behavioural and Social Sciences Ethical Review committee at the University of Queensland (#2010001493). Students scoring above the clinical cut-off at screening were immediately sent an automated personalised email inviting them to participate in the study. Eligible participants were randomly allocated in a 1:1 ratio to one of two parallel arms: LI-CBT or self-help control. Block randomisation in groups of four was used in order to prevent substantial imbalances between conditions. The random allocation was preprogrammed into the computer software used for assessments and randomisation. Provision of the self-help materials to the control group was automated and involved no interaction with participants. Participants were blind to the existence of the other arm of the study. Participants receiving LI-CBT were contacted by a wellbeing coach, either by email or telephone to set up their first appointment. The self-help control condition received their intervention—which consisted of a list of useful university websites, workshops, and services—by email. At 2, 6, and 12 month follow up, the full sample was emailed a link to the online follow-up surveys. All measures, except demographics, the K10, and the AUDIT, were included in follow-up assessments. If participants did not respond to two reminder emails at a follow-up time point, they were telephoned by a research assistant, blind to condition, in an attempt to obtain the data by phone.

Statistical Analyses The primary analyses involve multilevel mixed models regressions, using Stata® 13.0. This approach allows the incorporation of all available data in the analyses, without attempting any substitution of missing data. Results are presented with intention-to-treat data over the four time points and with the baseline score as a covariate for effects at 2, 6, and 12 months. Secondary analyses examine effects when LI-CBT participants who did not attend any treatment sessions were excluded. The displayed models assume independence of the covariance matrices. Results are presented in the form of regression coefficients (β), standard errors (SEs), and associated z-tests. Effect 3 147

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sizes are also displayed in shared baseline standard deviation (SD) units. A sample size of 176 participants was expected to allow detection of a medium effect size of Cohen’s f = .25 on a standard repeated measures analysis, with a power of .80, alpha of .05, and an assumption that the sphericity assumption was met.

Results Participant Flow The flow of participants is shown in Figure 1. Of the 1146 students who completed the screening measure, 91% (936) were eligible. However, only 11% of these (107) agreed to participate. Compared with all eligible students, consenting participants were significantly more distressed (t [1144] = 3.99, p < .001; Mparticipants = 27.65, SD = .72; Mnon-participants = 24.63, SD = .23). In the LI-CBT condition, 22 participants (42%) did not attend any treatment sessions. The remainder had 1–7 sessions, with a median of 4 (M = 3.8, SD = 2.0). Sessions were relatively brief (M = 21 min, SD = 25 min). Retention in

assessments was very strong: data from at least one posttreatment assessment were available on 96% of those in selfhelp group (n = 53) and 85% of LI-CBT participants (n = 44). Fewer participants from LI-CBT were assessed at 2 months (38, 73% vs 49, 89%, χ2[1] = 4.51, p = .034), 6 months (39, 75% vs 51, 93%, χ2[1] = 6.29, p = .012), and 12 months (44, 85% vs 53, 96%, χ2[1] = 4.35, p = .037) than from the control group, but all follow-up proportions were high. There were no significant baseline differences between retained participants versus those lost to follow-up assessments at 2, 6, or 12 months.

Sample Characteristics Despite random allocation, LI-CBT participants were more distressed at baseline on the K10 and more depressed on DASSDepression (Table 1). However, the conditions did not differ on any other measured variables. Most participants were full-time (92%), undergraduate (79%) students, and almost all were female (92%). Their average age was 23.0 years (SD = 6.4; range = 17–48), and most were single (78%). Students were

Assessed for eligibility (n = 1146)

Excluded (n = 1039) Not meeting inclusion criteria (n = 103) Declined to participate (n = 936)

Randomised (n = 107)

Allocaon Self-help control (n = 55)

LI-CBT (n = 52) Attended 1 session (n = 30) Did not attend sessions (n = 22)

Assessed n = 49 (89%)

2-month follow up

Lost to 2 months follow up n = 13

2.

Assessed n = 51 (93%)

Assessed n = 38 (73%) Lost to 2 months follow up n = 14

6-month follow up

Lost to 6 months follow up n = 4

Assessed n = 39 (75%) Lost to 6 months follow up n = 13

12-month follow up 4. Assessed n = 53 (96%)

Assessed n = 44 (85%)

Lost to 12 months follow up n = 2

Lost to 12 months follow up n = 9

Analyses Intention to treat analysis (n = 55) Per-protocol analysis (n = 55)

Figure 1

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Intention to treat analysis (n = 52) Per-protocol analysis (n = 30)

Flow of Participants.

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Table 1

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Participant Characteristics at Baseline

Categorical variables Gender Male Female Level of study First year undergraduate Subsequent undergraduate year Postgraduate Enrolment Full-time Part-time Student type Domestic International Marital status Unpartneredb Partnered

Continuous variables Distress (K10) Alcohol screen (AUDIT) Outcome variables Depression Anxiety and Stress Scales Depression (DASS-Depression) Anxiety (DASS-Anxiety) Stress (DASS-Stress) University Connectedness (UCS) Coping Self-Efficacy (CSE) Well-being (WHO5)

Self-Help (n = 55) n (%)

LI-CBT (n = 52) n (%)

χ2 (df)

p

3 (5%) 52 (95%)

6 (12%) 46 (88%)

. . .a

0.31

16 (29%) 29 (53%) 10 (18%)

20 (39%) 20 (38%) 12 (23%)

2.20 (2)

0.33

50 (91%) 5 (9%)

48 (92%) 4 (8%)

. . .a

0.99

45 (82%) 10 (18%)

43 (83%) 9 (17%)

0.02 (1)

0.91

41 (75%) 14 (25%)

43 (83%) 9 (17%)

1.05 (1)

0.44

F(1, 105)

p

Self-help (n = 55) M (SD)

LI-CBT (n = 52) M (SD)

26.49 (5.42) 4.31 (4.12)

28.88 (6.71) 4.69 (4.80)

4.14 0.20

.044 .658

14.04 (8.30) 11.31 (7.94) 19.60 (9.17) 75.95 (7.42) 123.82 (41.05) 10.02 (4.31)

18.62 (10.08) 14.69 (10.79) 20.08 (9.18) 79.13 (7.88) 125.65 (40.94) 8.81 (4.28)

6.61 3.44 0.07 4.64 0.05 2.12

.012 .066 .789 .033 .817 .149

Note. AUDIT = Alcohol Use Disorders Identification Test; CSE = Coping Self-Efficacy scale; DASS = Depression, Anxiety, and Stress Scales; K10 = Kessler 10; LI-CBT = low-intensity cognitive-behaviour therapy; M = mean; SD = standard deviation; UCS = University Connectedness Scale; WHO5 = 5-item World Health Organization Well-Being Index. a Via Fisher’s exact test. b One participant in LI-CBT was separated/divorced: other unpartnered participants were never married.

represented across faculties in the university and across year levels (first year undergraduate = 33.6%, later year undergraduate = 45.8%, postgraduate = 20.6%). Across the whole sample, 22% had a total AUDIT score ≥8, indicating a potential alcohol-related disorder. Within the LI-CBT condition, 17% of participants had a DSM-IV diagnosis, and 8% had two diagnoses. These included major depressive disorder (6%), generalised anxiety disorder (4%), obsessive compulsive disorder (2%), specific phobia (2%), alcohol abuse (2%), and bereavement (2%). Because diagnoses were made during the first LI-CBT session as part of the intervention, they are not available for control group participants.

including online interventions, 29% versus 0% (χ2[1, n = 76] = 13.49, p < .001), counselling services, 23% versus 2% (χ2[1, n = 76] = 7.54, p = .006), and the University Health Service, 20% versus 2% (χ2[1, n = 76] = 6.18, p = .013), while participants in self-help accessed greater support from university staff, 0% vs 15% (χ2[1, n = 76] = 5.56, p = .018). Over the follow-up period, LI-CBT participants continued to access online interventions at a higher rate than in self-help, 17% vs 2% (χ2[1, n = 72] = 4.68, p = .031), but other services were accessed at similar levels in the two conditions.

Service Utilisation

Analyses of Outcomes Using Intention-to-Treat on Allocated Participants

During the intervention period, significantly more students in LI-CBT accessed support services than the control group

Table 2 displays the equations produced by linear mixed models analyses on the four outcome variables, Table 3 shows the effect

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Table 2 Intention-to-Treat Analyses of Outcomes Using Multilevel Linear Mixed Models Regressionsa Prediction equations

Constant Coefficient (SE) Z (p) LI-CBT coefficient (SE) Z (p) Time 2 months coefficient (SE)2 Z (p) 6 months coefficient (SE) Z (p) 12 months coefficient (SE) Z (p) LI-CBT by timea 2 months coefficient (SE) Z (p) 6 months coefficient (SE) Z (p) 12 months coefficient (SE) Z (p)

Depression (DASS-Depression)

Anxiety (DASS-Anxiety)

Stress (DASS-Stress)

Connectedness (UCS)

Well-being (WHO5)

Coping Self-Efficacy (CSE)

14.036 (1.230) 11.41 (p < .001) 4.580 (1.765) 2.59 (p = .009)

11.309 (1.181) 9.57 (p < .001) 3.383 (1.694) 2.00 (p = .046)

19.600 (1.222) 16.04 (p < .001) −4.011 (1.924) −2.08 (p = .037)

70.672 (1.193) 59.25 (p < .001) 3.212 (1.711) 1.88 (p = .061)

10.018 (0.594) 16.86 (p < .001) −1.210 (0.852) −1.42 (p = .155)

123.818 (5.703) 21.71 (p < .001) 1.836 (8.181) 0.22 (p = .822)

−1.888 (1.508) −1.25 (p = .210) −3.510 (1.820) −1.93 (p = .054) −1.993 (1.496) −1.33 (p = .183)

−0.027 (1.316) −0.02 (p = .984) −2.045 (1.595) −1.28 (p = .200) −1.246 (1.306) −0.95 (p = .340)

−1.559 (1.430) −1.09 (p = 276) −4.694 (1.730) −2.71 (p = .007) −2.431 (1.419) −1.71 (p = .087)

3.054 (1.349) 2.26 (p = .024) 3.108 (1.665) 1.872 (p = .062) 1.624 (1.350) 1.20 (p = .229)

2.400 (0.661) 3.63 (p < .001) 4.193 (0.833) 5.03 (p < .001) 3.447 (0.678) 5.09 (p < .001)

18.349 (5.486) 3.34 (p = .001) 22.443 (6.834) 3.28 (p = .001) 24.931 (5.580) 4.47 (p < .001)

−5.068 (2.219) −2.28 (p = .022) −4.952 (2.809) −1.76 (p = .078) −3.623 (2.199) −1.65 (p = .099)

−4.673 (1.941) −2.41 (p = .016) −3.352 (2.466) −1.36 (p = .174) −3.716 (1.923) −1.93 (p = .053)

−3.000 (2.106) −1.42 (p = .155) −1.785 (2.672) −0.67 (p = .504) −1.208 (2.087) −0.58 (p = .563)

−5.57 (2.034) −2.74 (p = .006) −6.845 (2.584) −2.65 (p = .008) −6.105 (2.023) −3.02 (p = .003)

0.273 (0.949) 0.29 (p = .773) 1.086 (1.232) 0.88 (p = .378) 0.028 (1.020) 0.03 (p = .978)

−5.890 (8.174) −0.72 (p = .471) −2.919 (10.445) −0.28 (p = .780) 0.501 (8.238) 0.06 (p = .951)

Note. CSE = Coping Self-Efficacy scale; DASS = Depression, Anxiety, and Stress Scales; LI-CBT = low-intensity cognitive-behaviour therapy; SE = standard error; UCS = University Connectedness Scale; WHO5 = 5-item World Health Organization Well-Being Index. a Prediction equations in this table enable calculation of estimated means at each time point, by the addition of the relevant coefficients. Significant effects for time and condition by time coefficients in the table are shown in bold.

Table 3

Effect Sizes (d) between Groups and within LI-CBT

Measure

DASS-Depression DASS-Anxiety Connectedness (UCS)

Baseline to 2 months

Baseline to 6 months

Baseline to 12 months

Within LI-CBT

Between groups

Within LI-CBT

Between groups

Within LI-CBT

Between groups

−0.755 −0.498 −0.335

−0.550 −0.496 −0.742

−1.124 −0.575 −0.091

−0.538 −0.355 −0.911

−1.196 −0.746 0.224

−0.393 −0.394 −0.813

Note. CSE = Coping Self-Efficacy scale; DASS, Depression, Anxiety, and Stress Scales; LI-CBT = low-intensity cognitive-behaviour therapy; UCS = University Connectedness Scale.

sizes in shared baseline SD units, and Figure 2 displays the estimated mean scores. At 2 months, participants in LI-CBT showed significantly greater improvements than self-help controls on DASSDepression and DASS-Anxiety (Table 2), but the lack of subsequent differential rises in depression or anxiety shows that these differential changes were not maintained (Figure 2). In consequence, while there was a significant overall effect for time (Depression: χ2[3] = 27.12, p < .0001; Anxiety: χ2[3] = 15.10, p = .002), the overall group by time interaction fell short of the .05 level of significance (Depression: χ2[3] = 6.61, p = .086; Anxiety: χ2[3] = 6.94, p = .074). If the analyses were undertaken with the baseline score as a covariate, the key effect becomes the one for group because the focus is on post-treatment differences. In those analyses, 6 150

the overall effect for Anxiety retained the .10 level of significance (−2.881 [1.662] z = −1.73, p = .083), but the coefficient for Depression was −2.007 (1.973) z = −1.02, p = .309. There were no group by time interactions over the follow-up period. A significant differential effect was also seen for university connectedness, but this result was in the direction of the LI-CBT group having a differential reduction from baseline to 2 months that continued to 12 months, whereas the self-help group had a quadratic change reflecting an initial rise that was somewhat corrected at 12 months, albeit over a narrow range of scores (Figure 2). The overall time effect was not significant (χ2[3] = 2.98, p = .395), but the interaction of group and time gave χ2(3) = 13.38, p = .004. The effect was retained when the baseline score was entered as a covariate (−4.052 (1.930, z = −2.10, p = .036). Australian Psychologist (2015) Australian Psychologist 51 (2016) 145–153 © 2016 2015 The Australian Psychological Society

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20

16

18

Self-Help

16

LI-CBT

20 Self-Help

14

LI-CBT

12

14

10

12

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16 14

10

12

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0 Baseline 2 months 6 months

12 months

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0 Baseline 2 months 6 months 12 months

Baseline 2 months 6 months 12 months

DASS-Depression

DASS-Anxiety

DASS-Stress

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145 140 135 130 Self-Help

2

LI-CBT

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0 Baseline 2 months 6 months

12 months

Self-Help

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LI-CBT

120 Baseline 2 months 6 months

University Connectedness Scale

Figure 2

LI-CBT

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4

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Self-Help

8

6

6

12 months

Wellbeing (WHO-5)

Baseline 2 months 6 months

12 months

Coping Self-Efficacy

Estimated Marginal Means from Intention-to-Treat Analyses, of DASS-Anxiety, and the University Connectedness Scale.

No differential effects for group were obtained for DASSStress, WHO-5 well-being, or coping self-efficacy, but all three had significant overall effects for time (DASS-Stress: χ2[3] = 21.18, p = .0001; WHO-5: χ2[3] = 73.27, p < .0001; CSE: χ2[3] = 42.09, p < .0001). As Table 2 and Figure 2 show, both the WHO-5 and CSE showed improvements at all posttreatment assessments, but only the 6-month DASS-S score was significantly below the baseline level.

Analyses of Outcomes Using Excluding LI-CBT Non-Attendees Given that 42% of participants in LI-CBT did not attend any sessions, we also undertook analyses omitting those students. If we retained a correction for baseline scores, there was a significant differential effect from LI-CBT across the post-treatment Australian Psychologist Psychologist 51 (2015) Australian (2016) 145–153 ©2016 2015The TheAustralian AustralianPsychological PsychologicalSociety Society ©

assessments on DASS-Anxiety (−4.311 [1.744], z = −2.47, p = .013), but not for DASS-Depression (−2.430 [2.079] z = −1.17, p = .243). However, the regression also now showed a new effect in favour of LI-CBT on DASS-Stress (−4.011 [1.924], z = −2.08, p = .037). The estimated mean and SE for self-help across the post-treatment assessments was 17.73 (.899) and for LI-CBT was 14.04 (1.168). The previous differential effect for university connectedness in favour of self-help remained (−4.803 [2.101], z = −2.29, p = .022). There were no differential effects for condition on any other variable.

Discussion These results offer promise for a student-focused LI-CBT, especially in relation to anxiety, and especially in participants exposed to LI-CBT. Receipt of LI-CBT may also be associated 7 151

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with differential reductions in stress. Initial reductions were also seen for depression, but these appeared because of the higher baseline scores for that variable: Replication of the effects in a study with comparable initial scores on that variable would be required to have confidence in that result. Surprisingly, the LI-CBT group also had differential reductions in university connectedness. However, the self-help condition used university staff resources to a greater extent than LI-CBT participants, which may have staved off a reduction in connectedness that might otherwise have occurred. It remains to be seen whether if LI-CBT were provided as part of the standard university services, a different result may well have been obtained on that variable. Furthermore, the use of staff resources comes at both a financial and opportunity cost for the university: further research should examine the relative costeffectiveness of LI-CBT in comparison with standard services, to see whether improvements can be obtained at lower cost using if LI-CBT became standard practice. A significant issue for the broad potential impact of LI-CBT on the well-being of a university’s student body was the low consent rate for participation in sessions (11%) and the fact that 42% of consenting participants in LI-CBT who did not attend any sessions. A likely contributor to these rates was that details of potential benefits from LI-CBT were not provided prior to random allocation because of concerns about attenuating effects of self-help. Given the importance of motivation to the uptake of low-intensity interventions (Martinez & Williams, 2010), information about the benefits of participation may well have increased uptake. Substantially improved marketing of LI-CBT and higher rates of exposure to an initial session may be needed if substantial uptake by university students is to be obtained. The trial initially was focused on screening and engagement of students attending a university health service. There was some face validity in the use of this context for detection and support of distressed students: General practitioners are seen as the most preferred source for assistance with a mental health problem (Stallman, 2008, 2010a; Stallman & Shochet, 2009), and there appears to be less stigma attached to use of a primary healthcare service than to student counselling or mental health services. Furthermore, these students would already be seeking help for a health problem, which may imply some readiness to receive related assistance. Because around half of those who attend university health services for physical health problems report elevated levels of psychological distress (Stallman, 2008; Stallman & Shochet, 2009), the potential market for additional services is also large. However, this plausibility did not translate to high levels of uptake in the current study. Two potential reasons may have been the fact that the iPad kiosks in waiting rooms were in full view of other patients, and that use of the kiosks relied on signs adjacent to kiosks and on receptionists recommending their use. Web-based access in response to advertising throughout the university provided a more acceptable pathway for access, although numbers of participants remained limited. A totally web-based intervention, with the option of coaching support, may have had a different result. Strengths of the current study included the use of a manualised protocol, blind assessment, and the use of intentionto-treat analyses. While the flexibility that was given to wellbeing coaches produces some variability in the specific 8 152

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intervention that was used, reviews of sessions indicated a high level of fidelity to the protocol, and the approach in this study closely aligned with standard LI-CBT procedures (Richards & Whyte, 2009) and gives the study greater applicability to routine care. This first trial of LI-CBT within a university context demonstrated that it has potential to address students’ distress, particularly if issues of marketing and initial engagement are more effectively addressed. Robust results from a refined intervention and a larger sample would offer a sound basis for decisions about wide dissemination, especially if cost-effectiveness analyses were undertaken.

Acknowledgements We would like to acknowledge the Wellbeing Coaching team: Helen Perry, Daile Martin, Sharon Nugent, and Melissa Hatty for their involvement in the project and Genevieve Smith for assistance with preparation of the article. Funding for this project was provided by Australian Rotary Health.

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